Arti cial Decision Making Under Uncertainty in Intelligent Buildings

Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be increased. The rst is energy saving in the building. The second is customer value|how the people in the building experience the effects of the actions of the agents. We give evidence for the truth of this hypothesis through experimental ndings related to tools for articial decision making. A number of assumptions related to agent control, through monitoring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one uncertainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic decision situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.